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(1) Limited receptive fields and position information. Learning semantically consistent textures is difficult for vanilla CNNs, as they only have the local inductive priors and narrow receptive field of convolutional filters. Even the dilated convolutions [1]} are still incapable of tackling large corrupted regions in the images. Even worse, without explicitly encoding the positional information of corrupted image areas, the model is quite likely to just produce repeated and meaningless artifacts to fill large irregular masked regions.
